ArticleNature communications2025
ROSIE: AI generation of multiplex immunofluorescence staining from histopathology images.
Article in Nature communications, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
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- Trimodal, uncertainty-guided whole-slide framework for genome-scale spatial expression and image-only virtual perturbation in cancer cohorts.Genome medicine · 2026Article
- Cellular architecture and neighborhood-informed virtual spatial tumor profiling from histopathology.Cell · 2026Article
- Spotlight on challenges and novel methods in highly multiplexed tissue imaging-based spatial proteomics.Journal of translational medicine · 2026Review
- Histopathology-centered computational evolution of spatial omics: integration, mapping, and foundation models.Briefings in bioinformatics · 2026Review
- Dissecting and directing pathology foundation models.bioRxiv : the preprint server for biology · 2026Article
- Predicting Response to Immune Checkpoint Inhibitors in Melanoma: Emerging Approaches in Digital Pathology, Spatial Profiling and Machine Learning.International journal of molecular sciences · 2026Review
- An agentic framework for autonomous scientific discovery in cancer pathology.Nature medicine · 2026Article
- Review
- Digital Pathology and the AI-Based Quantification of the Tumor Microenvironment in Gastrointestinal Cancer: From Tumor Budding and Tumor-Infiltrating Lymphocytes to Tertiary Lymphoid Structures.International journal of molecular sciences · 2026Review
- A comprehensive survey of computer vision methods for spatial transcriptomics.Briefings in bioinformatics · 2026Review
- Systematically decoding pathological morphologies and molecular profiles with unified multimodal embedding.Nature methods · 2026Article
- HER2 Score-Aware Virtual Immunohistochemistry via Non-Contrastive Multi-Task Translation.Diagnostics (Basel, Switzerland) · 2026Article
- Deep learning-driven recognition of panoramic tumor microenvironment features in H&E sections and its application.Journal for immunotherapy of cancer · 2026Review
- Single-Cell and Spatial Omics: Methods and Applications.MedComm · 2026Review
- Genomic Characterization of Lung Cancer in Never-Smokers Using Deep Learning.Modern pathology : an official journal of the United States and Canadian Academy of Pathology, Inc · 2026Article
- miniMTI: minimal multiplex tissue imaging enhances biomarker expression prediction from histology.Research square · 2026Article
- Artificial intelligence models: transforming early diagnosis and precise treatment of gastrointestinal cancers.Molecular cancer · 2026Review
- Multimodal foundation models in colorectal cancer: from prediction to trustworthy clinical insight.Briefings in bioinformatics · 2026Review
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Abstract
Hematoxylin and eosin (H&E) is a common and inexpensive histopathology assay. Though widely used and information-rich, it cannot directly inform about specific molecular markers, which require additional experiments to assess. To address this gap, we present ROSIE, a deep-learning framework that computationally imputes the expression and localization of dozens of proteins from H&E images. Our model is trained on a dataset of over 1300 paired and aligned H&E and multiplex immunofluorescence (mIF) samples from over a dozen tissues and disease conditions, spanning over 16 million cells. Validation of our in silico mIF staining method on held-out H&E samples demonstrates that the predicted biomarkers are effective in identifying cell phenotypes, particularly distinguishing lymphocytes such as B cells and T cells, which are not readily discernible with H&E staining alone. Additionally, ROSIE facilitates the robust identification of stromal and epithelial microenvironments and immune cell subtypes like tumor-infiltrating lymphocytes (TILs), which are important for understanding tumor-immune interactions and can help inform treatment strategies in cancer research.
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